• Maximize
  • Time to think about a project
  • Reliability/Reproducibility
• Minimize
  • Data errors
  • Programmer/Analyst errors
  • Programming Time
  • Re-orientation time when revisiting

• Once we get a data set
  • Dig in!!
  • Start "playing" with tables and figures
  • Try models on-the-fly
  • Cut-and-paste into reports and presentations

• Once we get a data set
  • Dig in!!
  • Start "playing" with tables and figures
  • Try models on-the-fly
  • Cut-and-paste into reports and presentations

DON'T DO THIS!!

• 25 year study of rheumatoid arthritis
• 5600 individuals
• Several cool survival analysis models
• Needed data cleaning, validation and munging, and some custom computations
• Lots of visualizations

• Resulted in a muddle of 710 files (starting from 4 data files)
• Unwanted cyclic dependencies for intermediate data creation
• Lots of ad hoc decisions and function creation with scripts
• Almost impossible to re-factor and clean up
• Had to return to this project for 3 research papers and revision cycles!!!

• Yourself in
  • 3 months
  • 1 year
  • 5 years
• Can't send your former self e-mail asking what the f**k you did.

Biggest reason for good practices is

YOUR OWN SANITY

When you create a Project, the following obvious things happen:

1. RStduio puts you into the right directory/folder
2. Creates a .Rproj file containing project options
   • You can double-click on the .Rproj file to open the project in RStudio
3. Displays the project name in the project toolbar (right top of the window)

The following not-so-obvious things happen:

1. A new R session (process) is started
2. The .Rprofile file in the project’s main directory (if any) is sourced by R
3. The .RData file in the project’s main directory is loaded (this can be controlled by an option).
4. The .Rhistory file in the project’s main directory is loaded into the RStudio History pane (and used for Console Up/Down arrow o.mand history).
5. The current working directory is set to the project directory.
6. Previously edited source documents are restored into editor tabs, and
7. Other RStudio settings (e.g. active tabs, splitter positions, etc.) are restored to where they were the last time the project was closed.

I use Projects so that:

1. I'm always in the right directory for the project
2. I don't contaminate one project's analysis with another (different sandboxes)
3. I can access different projects quickly
4. I can version control them (Git) easily (topic for beyond this class)
5. I can customize options per project

I always work with RStudio Projects to encapsulate my projects.

However, each project needs to maintain a file structure to know where to find things

• Before you even get data
• Set up a particular folder structure where
  • You know what goes where
  • You already have canned scripts/packages set up
• Make sure it's the same structure every time
• Next time you visit, you don't need to go into desperate search mode

You can certainly choose your own organization for files

I have a suggested way:

# install.packages('devtools')
devtools::install_github('webbedfeet/ProjTemplate')
ProjTemplate::useTemplate()

• Use descriptive file names
• Be explicit
  • File1.R, File4.R won't help you
  • DataMunging.R, RegressionModels.R will
• Well-chosen names saves a lot of time and heartache

• Create at least a README file to describe what the project is about.
• I've started creating a "lab notebook" for data analyses
  • Usually named Notebook.Rmd
  • Either a straight R Markdown file or a R Notebook
  • Keep notes on
    • What products (data sets, tables, figures) I've created
    • What new scripts I've written
    • What new functions I've written
    • Notes from discussions with colleagues on decisions regarding data, analyses, final products

• Document your code as much as you can
  • Copious comments to state what you're doing and why
• If you write functions
  • Use Roxygen to document the inputs, outputs, what the function does and an example

You can load different packages in different scripts

Then you run a particular script and realize it needed a package you loaded in another script

You didn't realize it because the packae was already loaded when you developed the script

So I started putting all the package calls in a single place, in 'lib/pkgs.yml'

What's this pkgs.yml?!!!

• The name pkgs.yml is specific if you use my ProjTemplate pkg, which expects a lib/pkgs.yml

• You can also start it using, for example, ProjTemplate::add_package(c('tidyverse','ggplot2'))

• You can load all the packages using ProjTemplate::load_packages(). This will install packages if they're not already installed, and then load them into the current namespace

• Use scripts/functions to derive quantities you need for other functions
• Don't hard-code numbers

runif(n = nrow(dat), min = min(dat$age), max = max(dat$age))

rather than

runif(n = 135, min = 18, max = 80)

• This reduces potential errors in data transcription
  • These are really hard to catch

• If you're doing the same thing more than twice, write a function (DRY principle)
• Put the function in its own file, stored in a particular place
  • I store them in lib/R.
  • Don't hide them in general script files where other stuff is happening
  • Name the file so you know what's in it
  • One function or a few related functions per file
• Write the basic documentation NOW!

If you're using ProjTemplate:

1. Store all functions you will need as files in lib/R sub-directory of your main directory
2. Use ProjTemplate::reload() to load all the functions into a separate environment
   • Design choice so it doesn't clutter up your namespace
3. reload will also load packages at the same time.

Otherwise:

funcfiles <- dir('lib/R', pattern = '.R')
for(f in funcfiles){
  source(f)
}

The directory structure created and the way files are stored makes the lib folder easily convertible into an R package.

It has almost the right structure, just needs to become a package

See Hadley's book "R Packages", available for FREE here

See also, here, here and here

You can also create a package structure using RStudio Projects; just choose "R Package" instead of "New Project". It gets you exactly the right file structure

• Keep a pristine copy of the data
• Use scripts to manipulate data for reproducibility
  • Can catch analyst mistakes and fix
• Systematically verify and clean
  • Create your own Standard Operating Plan
• Document what you find
  • Lab notebook (example)

• The laws of unintended consequences are vicious and unforgiving, and appear all too frequenty at the data munging stage
• For example, data types can change (factor to integer)
• Test your data at each stage to make sure you still have what you think you have

• Typically:

Raw data >> Intermediate data >> Final data >> data for sub-analyses >> data for final tables and figures

• Catalog and track where you create data, and where you ingest it
• Make sure there are no loops!!

• Share initial explorations with colleagues so they pass a "sniff" test
  • Are data types what you expect
  • Are data ranges what you expect
  • Are distributions what you expect
  • Are relationships what you expect

• Are anomalies you find reasonable?
  • Instrument error
  • Data recording error
  • True outlier
  • Wrong study design
• This stuff is important and requires deliberate brain power
• May require feedback loop and more thinking about the problem

David Robinson, 2016

• I create separate files for creating figures and tables for a paper
  • They're called FinalTables.R and FinalFigures.R. Duh!

• This provides final check that right data are used, and can be updated easily during revision cycle

• It's a long road to this point, so make sure things are good.

The only way to learn R is to use R

• There are plenty of resources online
  • R-Bloggers
• There are user groups locally (the ones in DC is "Statistical Programming DC" and "R-Ladies DC")
• "Steal" code, modify it, and make it yours (All hail the glories of open source)
• Teach others

Send talks to your instructor before class starts

• 5-6 slides
• Demonstrates entire pipeline, from data ingestion to reporting
• Preferably from your own work, but other data is fine too if you can't share your own data
• I propose to put final slides as zip file on website, so we can all see what others are doing. If you don't want your slide up there, drop Abhijit a line (adasgupta@araastat.com)
• Slide file should be named "FirstName_LastName_BIOF339.pdf"